Population Assessment

Naturally, it is important to the study of the population impact of multiple sclerosis to first narrow the discussion to one representative county. In subsequent work, the framework thereby established can be expanded. Using the author’s current country of residence, the United States of America (US) simplifies matters, but there is little reason to prefer one particular state over another, and so, as preliminary research has focused on Texas, that is where the emphasis shall remain. Dallas County, located in Texas, is of particular interest because of its relatively poor health behavior statistics, ranking in at number 158 out of the 232 counties in Texas (County Health Rankings, 2013). If interventions for multiple sclerosis can be shown to have a reasonable likelihood for success in Dallas County, where the overall level of proper self-care is low, then the same interventions will almost certainly succeed elsewhere. Overall, however, the selection of a particular county is of less importance than the simple need to perform research in this area.

Multiple sclerosis (MS) has been chosen as a disease of interest to this study because of its chronic nature, which makes ongoing care and treatment more difficult to obtain. Unlike those with acute illnesses and diseases known to be fatal in the relatively short term, MS patients can be relatively ignored by charities and even by medical professionals, as it is less gratifying to aid someone with whom there will most likely be no dramatic positive results. In addition, the patients are neither “cute” young children nor elderly enough to garner pity, since most people diagnosed with MS are in their thirties or forties, with the average age of an MS patient being forty-five years (Stauffer, 2006, p. 3). Taken all together, these factors make MS the ideal disease for this type of study, for patients with it are inherently likely to be both vulnerable and underserved. In addition, possible interventions that might be taken can have a substantial impact here, given that mortality for MS patients is low; they can live into their early seventies or even longer, and the expected decrease in life span for someone with MS is only five to seven years (Stauffer, 2006, p. 4). This fact gives any input effort in providing treatments and interventions a great deal of leverage, amplifying the effect of whatever resources were put in. Having identified and justified both the choice of locale and the selection of the disease under study, the next single largest guiding factor in this research will be the decision of which framework to use.

Population impact assessment will prove invaluable in this work. The strategy has become a cornerstone of epidemiological work in recent years, and it is no less useful here than elsewhere. Verma et al. (2012) provided a good description of the basic technique, explaining that the framework explores the interaction of local population size, demographics, and availability to intervention techniques in order to select the ideal population for study. As described previously, some of these factors have already been taken into consideration here, and yet population impact assessment can be a potential area for subsequent analysis of data that goes into greater depth. To confirm that this approach is the best one, however, one must review the literature at hand.

Given the chronic nature of MS, it is informative to compare the proposed population impact approach for MS with the ways in which a similar tack has been taken with other chronic conditions. Of particular interest are affective conditions, as these are well known to often come hand in hand with MS. For example, Heller, Gemmell, and Patterson (2006) used population impact assessment to examine the role of both risk and intervention in depression and schizophrenia. Their results provide a valuable framework that highlights the ways in which the characteristics of a given population can help or hinder treatment programs. Likewise, there have been population impact studies done on tuberculosis, which, though an infectious disease rather than one arising from organic or unknown causes, is still similar to MS in that it can be recurring and often results in an unexpectedly high use of medical resources (Heller et al., 2006). It can only be hoped that this work will be able to suggest courses of action for the studied population just as previous work has identified such strategies for other diseases.

With location, disease, and framework determined, next remains an exploration of the interactions between all possible combinations of these three things. Noonan et al. (2010) provided one of the most recent assessments of prevalence of MS in Texas, finding 147 cases of women with MS out of a population of 214,235, and 35 cases of men with MS, out of 210,681 in 19 counties in northern Texas. These numbers will be valuable information going forward. Noonan et al. (2010) also discussed the geographic predictors of MS, noting that UV exposure, as assessed by the latitude of the community, is a predictor of MS diagnoses, with cases decreasing markedly as one gets closer to the equator. As far as interventions are concerned, this implies that simple sun protection may be a possible way to decrease risk. The number of cases in the study population was also significantly lower than similar study populations in Missouri and Ohio (Noonan et al., 2010, p. 3). This, too, is a fact for further examination, for it is not immediately clear why this should be the case. At this point in the research, identifying potential questions is as valuable as devising answers, and thus this is a good place to start.

References

County Health Rankings. (2013). Dallas County, Texas [Data file]. Retrieved from http://www.countyhealthrankings.org/

Heller, R. F., Gemmell, I., Edwards, R., Buchan, I., Awasthi, S. & Volmink, J. A. (2006). Prioritizing between direct observation of therapy and case-finding interventions for tuberculosis: Use of population impact measures. BMC Medicine, 4(35). doi:10.1186/1741-7015-4-35

Heller, R. F., Gemmell, I. & Patterson, L. (2006). Helping to prioritize interventions for depression and schizophrenia: Use of population impact measures. Clinical Practice & Epidemiology in Mental Health, 2(3). doi:10.1186/1745-0179-2-3

Noonan, C. W., Williamson, D. M., Henry, J., Indian, R., Lynch, S., Neuberger, J. & Marrie, R. (2010). The prevalence of multiple sclerosis in 3 US communities. Preventing Chronic Disease, 7(1)-A12. Retrieved from http://www.cdc.gov/pcd/issues/2010/jan/08_0241.htm

Stauffer, M. (2006). Understanding multiple sclerosis. Jackson, MS: University of Mississippi. Press.

Verma, A. Torun, P., Harris, E., Edwards, R., Gemmell, I., Harrison, R. A., . . . Heller, R. F. (2012). Population impact analysis: A framework for assessing the population impact of a risk or intervention. Journal of Public Health, 34(1), 83-89. doi:10.1093/pubmed/fdr026